{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducing Scikit-Learn\n",
    "\n",
    "# Scikit-Learn 简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There are several Python libraries which provide solid implementations of a range of machine learning algorithms.\n",
    "One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\n",
    "Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\n",
    "A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.\n",
    "\n",
    "Python中有许多软件包提供了一系列的机器学习算法实现。其中最知名的是[Scikit-Learn](http://scikit-learn.org)，它提供了大量的通用算法的高效实现。Scikit-Learn提供了一套干净、统一和流式的API，还有非常实用及完整的在线文档。这种统一性的优点在于，一旦你理解了Scikit-Learn其中一种模型的基本使用方法和语法，再去使用另一种模型或算法的切换过程基本是无痛的。\n",
    "\n",
    "> This section provides an overview of the Scikit-Learn API; a solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters.\n",
    "\n",
    "本节主要对Scikit-Learn的API进行总体介绍；对这些API的深入理解和掌握需要在后续的小节内容中使用更实际的例子来进行说明。\n",
    "\n",
    "> We will start by covering *data representation* in Scikit-Learn, followed by covering the *Estimator* API, and finally go through a more interesting example of using these tools for exploring a set of images of hand-written digits.\n",
    "\n",
    "我们首先从Scikit-Learn的*数据表示*开始介绍，接下来是*评估器*API，最后学习使用这些工具分析手写数字图像的数据集，这将会是一个更加有趣的例子帮助你理解这些工具和概念。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Representation in Scikit-Learn\n",
    "\n",
    "## Scikit-Learn 的数据表示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer.\n",
    "The best way to think about data within Scikit-Learn is in terms of tables of data.\n",
    "\n",
    "机器学习是有关从数据创建模型的技术：因此我们首先讨论数据是如何表示的，以方便被计算机理解。认识Scikit-Learn中的数据最好的方式是数据表。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data as table\n",
    "\n",
    "### 数据表\n",
    "\n",
    "> A basic table is a two-dimensional grid of data, in which the rows represent individual elements of the dataset, and the columns represent quantities related to each of these elements.\n",
    "For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\n",
    "We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library:\n",
    "\n",
    "基础的表是一个二维的数据网格，其中的行是数据集中每个独立的元素，而列是每个这些元素的属性值。例如我们之前使用过的[鸢尾花数据集](https://en.wikipedia.org/wiki/Iris_flower_data_set)，因为1936年Ronald Fisher研究过而闻名。我们可以使用[seaborn](http://seaborn.pydata.org/)来将这个数据集下载成一个Pandas的`DataFrame`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "iris = sns.load_dataset('iris')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\n",
    "In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``.\n",
    "\n",
    "这个数据集中每一行都代表一朵独立观察的花，所以数据集的总行数就是观察到的花的总数量。总的来说，我们将这些行组成的矩阵称为*样本*，总行数被称为`n_samples`。\n",
    "\n",
    "> Likewise, each column of the data refers to a particular quantitative piece of information that describes each sample.\n",
    "In general, we will refer to the columns of the matrix as *features*, and the number of columns as ``n_features``.\n",
    "\n",
    "同样的，数据集中的每一列都代表我们在每个样本中观测到的特征的数值信息。于是，我们将这些列组成的矩阵称为*特征*，总列数被称为`n_features`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Features matrix\n",
    "\n",
    "#### 特征矩阵\n",
    "\n",
    "> This table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*.\n",
    "By convention, this features matrix is often stored in a variable named ``X``.\n",
    "The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, and is most often contained in a NumPy array or a Pandas ``DataFrame``, though some Scikit-Learn models also accept SciPy sparse matrices.\n",
    "\n",
    "这样的表构造很清晰地表明信息是可以被想象成一个二维的数值数组或矩阵，也就是我们常说的*特征矩阵*。习惯上，特征矩阵通常被保存在变量`X`中。特征矩阵被认为是一个形状为`[n_samples, n_features]`的二维矩阵，而且一般都是保存在NumPy数组或者Pandas的`DataFrame`中，虽然一些Scikit-Learn模型也能接受SciPy稀疏矩阵作为输入。\n",
    "\n",
    "> The samples (i.e., rows) always refer to the individual objects described by the dataset.\n",
    "For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n",
    "\n",
    "这些样本（也就是行）永远指代数据集中的独立的对象。例如样本可以是花、人、文档、图像、声音文件、视频、天文物体或者任何你可以使用一组数值描述的物体。\n",
    "\n",
    "> The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\n",
    "Features are generally real-valued, but may be Boolean or discrete-valued in some cases.\n",
    "\n",
    "这些特征（也就是列）永远指代每一个样本中的一个观测的不同数据值。特征值通常是实数，在有些情况下也可能是布尔值或离散值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target array\n",
    "\n",
    "#### 目标数组\n",
    "\n",
    "> In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``.\n",
    "The target array is usually one dimensional, with length ``n_samples``, and is generally contained in a NumPy array or Pandas ``Series``.\n",
    "The target array may have continuous numerical values, or discrete classes/labels.\n",
    "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array.\n",
    "\n",
    "除了特征矩阵`X`，我们也通常需要*标签*或*目标*数组，习惯上我们称它为`y`。目标数组通常是一维的，具有长度`n_samples`，一般保存在一个一维NumPy数组或者Pandas的`Series`中。目标数组可能具有连续的数值或者离散的分类或标签。虽然一些Scikit-Learn评估器也可以处理二维的多目标值，形状为`[n_samples, n_targets]`的数组，但是我们主要聚焦在一维目标数组的通常应用场景中。\n",
    "\n",
    "> Often one point of confusion is how the target array differs from the other features columns. The distinguishing feature of the target array is that it is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n",
    "For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n",
    "\n",
    "通常让人混淆的一点是目标数组与其他特征列的区别。目标数组的区别特性表现在它通常是我们希望用来*预测数据*的量：在统计学术语中，它被称为因变量。例如，我们希望从上面的数据中构造一个模型用来从新的测量数据中预测鸢尾花的种类；在这个情况下，`species`列可以被认为是目标数组。\n",
    "\n",
    "> With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data:\n",
    "\n",
    "有了目标数组，我们可以使用Seaborn（参见[使用Seaborn进行可视化](04.14-Visualization-With-Seaborn.ipynb)）很方便地可视化数据：\n",
    "\n",
    "译者注：下面代码中的size参数已经过时，已经改为height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 525.85x432 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns; sns.set()\n",
    "sns.pairplot(iris, hue='species', height=1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):\n",
    "\n",
    "使用Scikit-Learn，我们可以从`DataFrame`中提取出特征矩阵和目标数组，我们可以使用一些我们在[第三章](03.00-Introduction-to-Pandas.ipynb)中介绍过的Pandas `DataFrame`技巧："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris = iris.drop('species', axis=1)\n",
    "X_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_iris = iris['species']\n",
    "y_iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To summarize, the expected layout of features and target values is visualized in the following diagram:\n",
    "\n",
    "下面的图大致画出了上面生成的特征矩阵和目标向量的情况："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/05.02-samples-features.png)\n",
    "[附录中生成图像的代码](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:\n",
    "\n",
    "数据格式已经准备好了，我们可以继续学习Scikit-Learn的评估器API："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-Learn's Estimator API\n",
    "\n",
    "## Scikit-Learn 评估器 API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
    "\n",
    "> - *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation.\n",
    "\n",
    "> - *Inspection*: All specified parameter values are exposed as public attributes.\n",
    "\n",
    "> - *Limited object hierarchy*: Only algorithms are represented by Python classes; datasets are represented\n",
    "  in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and parameter\n",
    "  names use standard Python strings.\n",
    "\n",
    "> - *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\n",
    "  and Scikit-Learn makes use of this wherever possible.\n",
    "\n",
    "> - *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value.\n",
    "\n",
    "Scikit-Learn API被设计成具有下述的指导原则，它们在[Scikit-Learn API文档](http://arxiv.org/abs/1309.0238)中有说明：\n",
    "\n",
    "- *一致性*：所有对象都共享一个公共的接口，从少量的一组方法中衍生出来，有着一致的文档。\n",
    "- *有限的对象层次*：只有算法被表达为Python类；数据集表示为标准格式（NumPy数组，Pandas `DataFrame`，SciPy稀疏矩阵），参数名称使用的是标准Python字符串。\n",
    "- *组合*：许多机器学习任务可以被表达为一系列的更基础算法，Scikit-Learn在任何可能的地方都在组合使用它们。\n",
    "- *明智的默认值*：当模型需要用户指定的参数时，软件包预定义了合适的默认值。\n",
    "\n",
    "> In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood.\n",
    "Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications.\n",
    "\n",
    "在实践中，这些原则令Scikit-Learn非常易于使用，一旦理解了基本的原则。Scikit-Learn中每个机器学习算法都是通过评估器API实现的，它为大范围的机器学习应用场景提供了一整套一致性的接口。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics of the API\n",
    "\n",
    "### API 基础\n",
    "\n",
    "> Most commonly, the steps in using the Scikit-Learn estimator API are as follows\n",
    "(we will step through a handful of detailed examples in the sections that follow).\n",
    "\n",
    "> 1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\n",
    "> 2. Choose model hyperparameters by instantiating this class with desired values.\n",
    "> 3. Arrange data into a features matrix and target vector following the discussion above.\n",
    "> 4. Fit the model to your data by calling the ``fit()`` method of the model instance.\n",
    "> 5. Apply the Model to new data:\n",
    "   - For supervised learning, often we predict labels for unknown data using the ``predict()`` method.\n",
    "   - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method.\n",
    "   \n",
    "最通常的情况下，你可以依照下面的步骤来使用Scikit-Learn评估器API（我们后面会按照这些步骤运行许多详细的例子）。\n",
    "\n",
    "1. 通过载入合适的Scikit-Learn评估器类选择一个模型的类型。\n",
    "2. 通过使用需要的值实例化模型对象来选择模型的超参数。\n",
    "3. 按照上面的方式将数据分为特征矩阵和目标向量。\n",
    "4. 通过调用模型实例的`fit()`方法将你的模型按照数据进行拟合。\n",
    "5. 将拟合后的模型应用在新的数据上：\n",
    "    - 对于有监督学习，通常我们使用`predict()`方法来预测未知数据的标签。\n",
    "    - 对于无监督学习，通常我们使用`transform()`方法来转换或推断数据的属性。\n",
    "\n",
    "> We will now step through several simple examples of applying supervised and unsupervised learning methods.\n",
    "\n",
    "下面我们通过几个简单的例子来使用有监督和无监督学习方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised learning example: Simple linear regression\n",
    "\n",
    "### 有监督学习例子：简单线性回归\n",
    "\n",
    "> As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\n",
    "We will use the following simple data for our regression example:\n",
    "\n",
    "作为这个过程的一个例子，让我们考虑简单的线性回归，也就是最常见的将一根直线拟合到$(x, y)$数据上。我们使用下面简单的数据来作为回归的例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "x = 10 * rng.rand(50)\n",
    "y = 2 * x - 1 + rng.randn(50)\n",
    "plt.scatter(x, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With this data in place, we can use the recipe outlined earlier. Let's walk through the process: \n",
    "\n",
    "有了数据之后，我们就可以按照刚才的步骤来实现回归。让我们一步一步的来操作："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Choose a class of model\n",
    "\n",
    "#### 1. 选择模型类型\n",
    "\n",
    "> In Scikit-Learn, every class of model is represented by a Python class.\n",
    "So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class:\n",
    "\n",
    "在Scikit-Learn中，每个模型类型都是一个Python类。因此如果我们希望计算简单的线性回归模型，我们可以载入线性回归类："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html).\n",
    "\n",
    "注意还有更多通用的线性回归模型；你可以在[`sklearn.linear_model` 模块在线文档](http://Scikit-Learn.org/stable/modules/linear_model.html)中学到更多的内容。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Choose model hyperparameters\n",
    "\n",
    "#### 2. 选择模型超参数\n",
    "\n",
    "> An important point is that *a class of model is not the same as an instance of a model*.\n",
    "\n",
    "要记住的一个重要的点是*一个模型的类别与一个模型的实例不是同一个东西*。\n",
    "\n",
    "> Once we have decided on our model class, there are still some options open to us.\n",
    "Depending on the model class we are working with, we might need to answer one or more questions like the following:\n",
    "\n",
    "> - Would we like to fit for the offset (i.e., *y*-intercept)?\n",
    "- Would we like the model to be normalized?\n",
    "- Would we like to preprocess our features to add model flexibility?\n",
    "- What degree of regularization would we like to use in our model?\n",
    "- How many model components would we like to use?\n",
    "\n",
    "我们决定了我们模型类别之后，还有一些参数可以进行选择。取决于我们选择的模型类别，我们可能需要回下面一个或多个问题：\n",
    "\n",
    "- 我们需要拟合偏移（例如y截距）吗？\n",
    "- 我们需要模型归一化吗？\n",
    "- 我们需要预处理特征来增加模型的灵活性吗？\n",
    "- 在我们的模型中正则化的角度是多少？\n",
    "- 我们想要使用多少个模型的组件？\n",
    "\n",
    "> These are examples of the important choices that must be made *once the model class is selected*.\n",
    "These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\n",
    "In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\n",
    "We will explore how you can quantitatively motivate the choice of hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\n",
    "\n",
    "一旦模型类别选定后，上面这些都是一些重要的选择。这些选择通常被称为*超参数*，或者模型拟合数据前被设置的参数。在Scikit-Learn中，超参数通过实例化模型传递参数值来设置。我们会在[超参数和模型验证](05.03-Hyperparameters-and-Model-Validation.ipynb)一节中深入讨论如何定量掉着那个这些超参数的值。\n",
    "\n",
    "> For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter:\n",
    "\n",
    "对于我们线性回归例子来说，我们可以实例化`LinearRegression`类并且使用`fit_intercept`参数来设置你是否希望拟合截距值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression(fit_intercept=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n",
    "In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*.\n",
    "\n",
    "记住当模型被实例化后，唯一的动作就是保存了超参数的值。也就是说我们还未将模型应用到任何数据上：Scikit-Learn API将*模型选择*和*将模型应用在数据上*区分的很清楚。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Arrange data into a features matrix and target vector\n",
    "\n",
    "#### 3. 将数据组合成特征矩阵和目标向量\n",
    "\n",
    "> Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n",
    "Here our target variable ``y`` is already in the correct form (a length-``n_samples`` array), but we need to massage the data ``x`` to make it a matrix of size ``[n_samples, n_features]``.\n",
    "In this case, this amounts to a simple reshaping of the one-dimensional array:\n",
    "\n",
    "前面我们详细介绍了Scikit-Learn数据表示，它需要一个二维的特征矩阵和一个一维的目标数组。这里我们的目标变量`y`已经是正确格式了（长度为`n_samples`的数组），但是我们需要将数据`x`变成一个形状为`[n_samples, n_features]`的矩阵。在这个情况下，我们需要将一个一维数组进行变形："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = x[:, np.newaxis]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Fit the model to your data\n",
    "\n",
    "#### 4. 将模型拟合数据\n",
    "\n",
    "> Now it is time to apply our model to data.\n",
    "This can be done with the ``fit()`` method of the model:\n",
    "\n",
    "现在是时候将我们的模型应用在数据上了。这可以通过模型的`fit()`方法实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
    "In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following:\n",
    "\n",
    "执行`fit()`方法是会导致一系列的模型内部计算，计算得到的结果会保存在模型对象的属性上，用户可以查看它们。在Scikit-Learn中习惯上所有通过`fit()`过程学习得到的模型参数都有下划线后缀；例如在这个线性模型中，我们有下面的属性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.9776566])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9033107255311164"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> These two parameters represent the slope and intercept of the simple linear fit to the data.\n",
    "Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n",
    "\n",
    "这两个参数代表着我们拟合数据后得到的斜率和截距。回想我们的数据定义，我们很容易看出它们很接近输入的斜率2和截距-1.\n",
    "\n",
    "> One question that frequently comes up regards the uncertainty in such internal model parameters.\n",
    "In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves: interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\n",
    "Machine learning rather focuses on what the model *predicts*.\n",
    "If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/).\n",
    "\n",
    "一个经常被提到的问题就是关于这样的模型内部参数的不确定性。通常来说，Scikit-Learn不提供工具来对内部模型参数本身进行概括：解释模型参数更多是一个*统计模型*问题而非一个*机器学习*问题。机器学习更加聚焦的是模型*预测*的内容。如果你希望深入了解模型拟合参数的含义，可以使用别的工具，包括[统计模型 Python 包](http://statsmodels.sourceforge.net/)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Predict labels for unknown data\n",
    "\n",
    "#### 5. 对未知数据进行预测\n",
    "\n",
    "> Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\n",
    "In Scikit-Learn, this can be done using the ``predict()`` method.\n",
    "For the sake of this example, our \"new data\" will be a grid of *x* values, and we will ask what *y* values the model predicts:\n",
    "\n",
    "一旦模型训练好了，有监督机器学习的主要任务就是用它来评估不属于训练集的数据结果。在Scikit-Learn中，可以通过`predict()`方法来实现。在这个例子中，我们的“新数据”是一个*x*值的网格，我们使用模型来预测出相应的*y*值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit = np.linspace(-1, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xfit = xfit[:, np.newaxis]\n",
    "yfit = model.predict(Xfit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, let's visualize the results by plotting first the raw data, and then this model fit:\n",
    "\n",
    "最后，让我们在图表中画出原始数据的散点和新数据的直线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example\n",
    "\n",
    "模型的性能可以通过比较结果和已知的基线进行评估，我们会在下一个例子中看到。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised learning example: Iris classification\n",
    "\n",
    "### 有监督学习例子：鸢尾花分类\n",
    "\n",
    "> Let's take a look at another example of this process, using the Iris dataset we discussed earlier.\n",
    "Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\n",
    "\n",
    "让我们在通过一个例子来介绍这个过程，本例中我们使用前面的鸢尾花数据集。我们的问题是：给定鸢尾花数据集的一部分用来训练模型，我们能多好的预测剩余数据的标签？\n",
    "\n",
    "> For this task, we will use an extremely simple generative model known as Gaussian naive Bayes, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\n",
    "Because it is so fast and has no hyperparameters to choose, Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models.\n",
    "\n",
    "对于这个任务来说，我们会使用一个极端简单的生成模型，称为高斯朴素贝叶斯模型，它的算法思想就是假设每个分类都可以从轴对齐的高斯分布获得（参见[深入：朴素贝叶斯分类](05.05-Naive-Bayes.ipynb)）。这个模型速度极快并且没有需要选择的超参数，因此高斯朴素贝叶斯经常可以用来作为一个基准分类模型，在我们使用更复杂的模型进行性能优化之前优先使用它。\n",
    "\n",
    "> We would like to evaluate the model on data it has not seen before, and so we will split the data into a *training set* and a *testing set*.\n",
    "This could be done by hand, but it is more convenient to use the ``train_test_split`` utility function:\n",
    "\n",
    "我们希望通过模型没有训练到的数据对它的性能进行评估，因此我们需要将数据分为*训练集*和*测试集*。这可以通过手工完成，但是还可以使用`train_test_split`工具函数很方便的实现：\n",
    "\n",
    "译者注：下面代码将过时的cross_validation修改为model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\n",
    "                                                random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With the data arranged, we can follow our recipe to predict the labels:\n",
    "\n",
    "数据准备好后，我们可以依照步骤对标签进行预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB # 1. 选择模型类别\n",
    "model = GaussianNB()                       # 2. 实例化模型\n",
    "model.fit(Xtrain, ytrain)                  # 3. 拟合数据\n",
    "y_model = model.predict(Xtest)             # 4. 预测新数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:\n",
    "\n",
    "最后，我们可以通过`accuracy_score`工具来查看有多少比例的标签我们是预测正确的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!\n",
    "\n",
    "准确率高达97%，我们可以看到对于这个数据集来说即使如此简单的分类算法也可以非常有效。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning example: Iris dimensionality\n",
    "\n",
    "### 无监督学习例子：鸢尾花数据集降维\n",
    "\n",
    "> As an example of an unsupervised learning problem, let's take a look at reducing the dimensionality of the Iris data so as to more easily visualize it.\n",
    "Recall that the Iris data is four dimensional: there are four features recorded for each sample.\n",
    "\n",
    "作为无监督学习问题的例子，我们来看一下对鸢尾花数据集进行降维处理令它们更容易可视化。我们都已经知道鸢尾花数据集有四个维度：也就是每个样本都记录了四个特征的数据。\n",
    "\n",
    "> The task of dimensionality reduction is to ask whether there is a suitable lower-dimensional representation that retains the essential features of the data.\n",
    "Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or higher!\n",
    "\n",
    "降维的任务是找出是否有一种合适的低纬度数据表示能基本保留了数据的关键特征。通常降维都被用来帮助数据可视化：毕竟在二维数据上作图肯定比在四维甚至更高维度上作图容易的多。\n",
    "\n",
    "> Here we will use principal component analysis (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique.\n",
    "We will ask the model to return two components—that is, a two-dimensional representation of the data.\n",
    "\n",
    "这里我们会使用主成分分析（PCA；参见[深入：主成分分析](05.09-Principal-Component-Analysis.ipynb)），它是一个快速的线性降维方法。我们会要求模型返回两个组成部分，即数据的二维表示。\n",
    "\n",
    "> Following the sequence of steps outlined earlier, we have:\n",
    "\n",
    "依照前面介绍的步骤，我们可以："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA  # 1. 选择模型类别\n",
    "model = PCA(n_components=2)            # 2. 实例化模型，设置超参数\n",
    "model.fit(X_iris)                      # 3. 拟合数据，注意这里没有y参数\n",
    "X_2D = model.transform(X_iris)         # 4. 将数据转换为二维"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:\n",
    "\n",
    "下面绘制结果。最简单的方式是将结果插入回原始的鸢尾花`DataFrame`，然后使用Seaborn的`lmplot`来展示结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lZuvPloPeUEfd2v+h8Wtze7VyMj3F6K0NR1dggBn0o3qK+/w19fb7kcrLw/35s0lmXD3FIcnNWixzkbmsrIwDBw6g6zoAuq5z8OBBysrKYp5XWlqK3W4HYNKkSZSVlfHpp5+mPd7+ilxmHlrowjBMVAWG5DlRVUXugQ0g0Vt/iqKEE5BmCz/eC/axF4IRwgz6MU0TM+gHIxR+PA2sennYqnGJ9LDMCqy4uJjRo0ezcuVKZsyYwcqVKxk9enSn7cMDBw4wbNgwAHbs2MGXX37JiSeemImQ+y36MnOknL7Q4+j2HljH0nu5M2ZtRvMhcHhiHlNsTvTmQ716nfBq7ZqMVSFa9fKwVeMS6WGZBAZw3333MW/ePJ566iny8/NZsGABANdffz0/+tGPOP3003nsscfYvn07qqpit9tZuHAhpaWlGY48PTrOEYt0rgepWLQqNa8Uo7UBorf+Qn7UvN7/nbWXj81YwYZVLw9bNS6RHpZKYCNHjmTp0qWdHn/22Wfb/38kqQ0UvUlK0XPEAOlcnwXsYy/Ev/lFzCBgc0AoAIqRtq2/ZLHq5WGrxiXSw1IJbDCKl5Qa/SGeef1j3C5bzDZhpPQ+mnSut7Z4W3/Fk2fjzT+l03NTfVG5Y7HD5WOnc5zthIQ+16qXh60al0gPSWAZ1jEpeX1BmtuCYJqUFLpiVmTRc8QipHO99XXc+vOU5uHtUOmW6ovK8cadPPe/r/CtkZcldFZk1cvDVo1LpIcksAzrmJSavEEwwW7TwmMp7BpNAZ1nXv8YTVPw+XVy3Xby3HbpXD+ApPqicrxiB51Qr4odrHp52KpxidSTBJZh0cMtHTaVYMhAwSTfE/6Hps0foqnVj4nCcYUemtQALW1BdN1geIlHqhAHiHjVitgc4ceTIG6xg+bodbGDDJIUViIJzAKcdo0D9V5Awa4puF2O9uGWTa2B8OM2FUVRKMh14nLaKPQ4uP2q3nVzENYVr1qRUKBP1YrxxC120AMUu4oSTkr9nbosyU8km2UuMg9GkQrEkGFSVuKhuNCF066hGyb+oI5pmgRDOiiQ77a3f54Ubgw8qb6oXFk+Bd3Q8esBTNPErwcIGjqnFI5M+CJwf6Yuy4VjkQqSwDIougIxct7lzrGTnxNeYXl9IZx2G3k5dtyuowlMCjcGHnv5WJyTrkH1FEKgFdVTiHNS8jrNjympYM6omRQ48vGG2ihw5HPdmf+PTxuqE05Kdb56HKo95rFE71x1TH6GadAYaOaZj37Hoq1P8/d9HyXl6xSDi2whZlBXZfFeX4j5R7YHI6s0f1DHYVOlcGMAS/VF5Y7FDqWleTzz1yUJXwTu6c5Vd1uE0WdwbSEfh30NRLqR9rYiUogISWBpEq8FVCJl8ZECDWkflZ3i3e2idFKmw2rXXVLqmJBOKRzJlv0fxL1z1dP5WPT7NAVaQFFQTLN9VdbbikghQBJYWkR321AU2qcvD8l1EDTCz+ludRXpmSiyS+Rul2no4G9FbzmMfmAXdd69UDE90+EBXV8EjpyNRSekLfs/YMIx4/m0obrTKmvR1qe77UkY/T5BPdg+FibPHh5t1JeKSCEkgaVB5KzLMEwON/sxDBPDhLrmAABtvhAup8axUhY/oAS3rQknr7YmUBRQNTB1mt77I073cEsMouzqInBXTXI/bajmlnHf7/Q6PfUkjHmftsPhilpHHm57+HMiFZFC9IYksDSInHUdPNzWnryiBXWDHMUmyWuAMZoPgb81nLzaB1GqmIZhqUnK8S4Cv1L1WtyEtLd1P4u2Pt1pBZZIT8LI+0RvN5qmScAIYiomlSdK+yfRO1KFmAYlBS4CIYOQbtBxjGbknzWfP8TaLTXpDk2kkJpXCnqIo3/KACaKZk/aBeVUKXYVETCCMY81BVrw6f64pfDxyvS76knYVUWknH+J3pIVWBpEum2oikLIjE1hJuEfznXDkLtdA4x97IXoB3aBqRP+WTE8eVtxujHtLrwrF2Rktlci4p2NeUNecu2eTtuKK6rX4LG78esBQqEQmqIx3HNMtxeV41VEWmVyt8geksBSLFJ96A/oR3eROlAVBU1V5G7XAGMvH4v+1UsI/t9KMAzQbOB0g6FjtjVj6HqfG/emunN9vLMxb7CNPHtsuyvd0Dnkq6PUXUyhMz+mMlFWVCLVJIGlUKT6MBgy8AcNgrqJqigYUaswVQEwcTntcrdrAHKdOQOt9MSYZKOGvJj+QJ8b96a6c31Ex1XSoq1Pdzrnagw0Z81EZGllNfDIGVgKrd1SQzBk0NwWRDcMtCNn+ZqqUJznwK6paKpCWbGHay+qkAKOAcpePhb3JXeQe+WjuC+5A9PfFh5uGa0XjXujO9crihJOhKot/HgKdXXOVWDPi3meFSciSyurgUlWYClU2+ijza+jEN4mBNCOnHeVDnHzyM3SjHcwshUORW+o67Zxb3dbhPE61+90qWzSaml4779TtrqIt62ooaGjxzzPihORu7oWYMWVokicJLAUKilwUd/sR4s6+zJMsGnhZrzxunPIKmzgc55wGr4v/ogZORdzuFE0W3vj3p62CDt2rt9pC7HCFcSm9K1LfG903FaMrGysPhG5p3tqIjvJFmIKTZ9QjqYq7fe+wr+auF12XA6NxeuraGgN4HbZ2icvb6uuzWTIIsWCNdto3fY2uPLCyUsPgb8F26hz2ldYPW0Rduxcv9ERQMPE6S7sdZf4aNtrd7Jo69Pc895/s2jr0wltr8UriZ8zKvmJs7/iXQuw4kpR9I6swFJo7MgSLv5aOaver0E3DGyaitvlwKYpYJrtneghPBPMT/jcTFZhA1dw2xoUzYZqy4GcfADMoB9j305gBtDzcMtworumfYuxvsCGx1WA4ji6wujt6qI/s76yYSJyVy2zrLZSFL0jK7AUu+yck7h59mmcclwheW4Hw4bkMHfaKHxBA4ct9tsvc74GPqP5EIrNGftghwIONa8UQoHY53Q4I4suDCktOpGgLfZn0d6uLvoz6ysbZMtKUfSOrMDSIF4z3pKCmm470cv5WHbq6X6WmleK6W8CJWquVsfkNPbCcBPgIOFqxVCg2+GWvVldRJeSH5NfypRjzmFMScWgOCPKhpWi6B3tvvvuuy/TQaRbW1sAs2NPpyTweJx4vYGenwjkue38Y1cthhkuq490op89+SQOHPayeH0VgZCBy6HR6g/xj121DBuSw7Aid9JjSSWrxAGpj6W9+3woAPYcTH8res3/oRQcg1YwLPwkVz7GP/+BoRvh5r5HkpPjrMvbn6MVDEMpOAbz8BeYbQ2oucU4zrq8yzteQ90lDM0pYW/LfpoCzQxxFnLpSdM7/WMd2SYMGEFcmpO2UBvbDn3M0JwS9rcexBtqw6ZG/UBlBLErdv5+cBurd69n26GPybPnMtSd/B+krPL3pKc4PB5nlx8T6ScrsAzpbs7XwiVb5XwsC0UXXwBxLyjby8dSUJhD7aZl3XbR6O1wy0RWF51KyW1OQrrBhpqN8VtHBdtQFAUdPeXVjUL0hSSwDOlui7CrSc1yPmZtPRVfRHhGjsObf0oaIwvrbpsw3h0vm2IjZIbk7pSwLElgGRA94DK6hB7CK7NEJjUL64ice5neRvA2gWfI0YrADudbmdTTyJOOq7h73vvvAX8uJrJbwlWIjY2NcR/fv39/0oIZLCIDLp12LVzxZdfQNLV9nMr0CeXouoE/qIdb9gT1uJOaReZFzr2M1gbIKQw36m2uxfB7MYP+bosv0q1TK6iQv73YI94dMLk7JayuxwS2e/duLrzwQiZMmMA3vvENVq9eHfPxiy66KGXBWcm26loWLtnK7b96j4VLtvbrwnFtoy9uCf2Xta0sXLKVl9ZV4bSp2FQFry9EocfB3Gmj5PzLgqLPvVSXB3KLw8UZ3sOonkKck5LbYLc/OpaSF+YUMGfUTIC4fQJPKRyZ8IwvITKhxy3Ehx56iOnTp/Pd736Xv/3tb8yfP58vvviCG264AQAzFeV8FtPTll9vxdsibGoN4PPr7Z05AiEDPaRz9fmSuKys47mX6nRjOnIg0Ir7kjsyGFms1Z9t4K0vNuEPBXDaHEw9bjL/OmEWhw41s2jr03H7BH7aUM2cUTOlg/sA9/LLL+NwOLj88sszHUqv9ZjAPvzwQ55++mk0TaOyspLTTjuN6667jtbWVm699dZ0xJhx0Vt+0PeqwEjhxt7aVtoCOh6XjXyPg0DIoNUXItdtl8rDLNOxLyFgqXMvCCevNXs2oCgKqqIS0IOs2bMBT66DKUMn91jcIQlrYLvyyiszHUKf9ZjAVFWltbWV/Pxw25tjjjmGF198sT2JDQbJqAqMXsUV5jnRvEFavEF0w+TYEg/etiB5bnvM50TeQy41W1dvLx1nwltfbEJRFDQlsm2toGOw6pM/MWXo5B6LO0Tmeb1ebr31Vvbu3Ytpmlx99dWsWrWKiooKPvzwQxoaGvjpT3/KpEmTMAyDRYsWsXnzZvx+P+effz4//OEPAXjjjTd49tlnARg+fDi//vWvefLJJ3E6ndxwww18+eWXzJ8/n0OHDqGqKnfddRfjxo3jr3/9Kw899BAQ3nV75plnOOaYYzL2/YjoMYGdccYZrF+/PmZ5WVRUxG9/+1uuvfZafL6BX9qdjKrAjqu4fI8Dp0Oj0OPg9qvGsXDJ1rjvEWn6m6ztS5FcHfsSpmI6cn/5QwFUJfbMVUGhLeQHpE9gNnj33XcpKSnh6aefBqCpqYlVq1bR2NjI73//e6qrq7n22mtZv349r7/+Opqm8Yc//AFd17nxxhv5y1/+QklJCU8++SQvv/wyxcXFHD58uNP73H333dx9992MHDmSL774guuuu44333yT559/np/+9KeMHz8en8+HqlqjC2GPCez222+nqamp0+P5+fm88MILbNiwISWBWcn0CeUsXl+Fn/CqKNI1ozdVgT2t4rp6D5uiyqVmi+vtpeNU6zh52Kba0E0dODrXx8Qk50hPxnh3wOSsy1oqKip45JFHePTRR5k0aRITJ04E4LLLLgNg5MiRlJWV8dlnn7Fp0yY++eQT/vSnPwHh1duePXv49NNPmTZtGsXFxQAMGTIk5j1aW1v54IMP+I//+I/2x7xeL01NTYwfP55HHnmECy+8kMrKSo477rh0fNk96jGBjRgxosuP6bo+KLYRu+ua0ZWO234uh0YgZHS5iuvqPV5aVyWXmgegnnom9lW8rvKaohI8Ug6voGBiYpomF596XvvnyVmXtZ1wwgn88Y9/ZNOmTTz77LPtCwdFUTo91zRN5s2bx7nnnhvz+Isvvhj3+dGf5/F4WLFiRaeP/du//RtTpkzh3Xff5ZprruGxxx7jjDPO6OdX1X+9vsis6zrvvPMOK1as4O2332bEiBHMnTs3FbFZSryGvF2JV7XobQvCkb88Xa3i+tL0V2Sf1uqt3Q6s7I94k4fznXnk6Dm06W0xVYjfGnMxhw41J+ErEql24MABCgoKuPTSSykrK2PhwoU4nU7eeOMNJk2axO7du9m/fz8nnXQSkydPZsmSJUyaNAmHw8GBAwfQNI2JEydy00038b3vfa99CzF6FZabm8uJJ57I8uXLmTkzfL1i+/btjBkzhs8//5xTTjmFU045herqanbu3JldCWz79u0sX76cVatW4fP5CAQCPPHEE0ydOjWV8WWleFWLADYFct2OXhVjJGP7UlhL4/sreuyZ2FddVRR6g20cn3ts+xbhCfnW2AISifn0009ZsGABqqqiqiq33347Tz31FEOHDuXb3/42DQ0NPPTQQzgcDr71rW+xf//+9roFt9vNggULOPnkk/nhD3/Id7/7XRRF4dhjj+VXv/pVzPs8+uijPPDAAzz//PMEg0HOPPNMHnzwQV588UXef/99bDYbZWVlXHrppZn4NnSimD1c5HruuedYvnw5e/bsYdKkSVx66aVMnTqVadOmsWLFivb91GxSV9eCYST//lppaR6HDjVz+6/ew+2yxSzXTdPE6wux8Kav9/p1+1KFGIkl06wSB1gnlrZXbke35XT6+0GgldwrH+3Xay/a+nSnisKmQAutwVaKXEPQDZ3GQDO6oXNcwTFcckLnrvWZYJU/m57iKC3NS2M03bvmmmv4yU9+wqxnBUgAACAASURBVFe/+tVMh5IxPa7AHnnkEQoLC1mwYAEXXnhht3uoIizZvQx7s30prM9WOBS9oS4ld8fiVRS2BFvx2NwYpkGDvxEUBUVR2N9ySLrLi6zWYy3kb3/7W84991zuvvtuJk+ezMMPP8xHH32UjtiylvQyHByCNdvwrlxAy8u34V25gGDNtoQ+r2DiDDBCmEE/pmkmtWdivMnDLs1JviOXpkALKAoq4f8ZpjGgpi4PNi+++OKgXn1BAiuwCRMmMGHCBO69917Wrl3LihUr+N3vfodpmvz+97/nqquu6lSOOdglUrUol5OzW6SJb18KMTwjx+GclLq7Yx0rCiPbiiEj1H4fzMTEptqku7zIagkXcbhcLmbOnMnMmTPZv38/y5cvZ8WKFTzzzDP84x//SEowu3fvZt68eTQ0NLRvW3Ys49d1nQcffJB3330XRVG44YYbuOKKK5Ly/snU3bZfsnsrivRLZHhld9J5dyyyragqKoZpoAAmUODMk44bIqv1aR7YMcccw4033siNN96YtOQFcO+993LVVVcxY8YMVqxYwT333MPvfve7mOe88cYb1NTUsG7dOhoaGpg5cyYTJ060zMW6RCSrt6LInESHV1pB5KLyiuo17Gs9gKpqFNrzsKka/mCwXx03Ol6algvQIp16PAN76623uOeee+J+7J577qGhoSEpgdTV1fHxxx9zySWXAHDJJZfw8ccfU18fu72xevVqrrjiClRVpaioiMrKStauXZuUGNKlq3Eqcjk5e6h5peG+h9Es1sQ32piSCu6acCs3jv0uJ+aXYypm+ziVviacyKXpjmNYttfuTHL0QsTX4wrsN7/5Dbfcckvcj1122WU88cQTTJnS/55p+/btY9iwYWhaeFWiaRpDhw5l3759FBUVxTxv+PDh7b8vKyvr9VDN4uLcfsfblUTKbMtKcznc1IYrqkrRFwhRVpqb1DJdq5T8WiUOSF4srZNnU7f2f8AMoticmCE/KAbFk2fjSeA9MvU9+WbpWXxz9FlJea2NH/0Zp92O80hLKjs2/CE/G/f/uU/vYZW/J1aJQ/SsxwRWXV3N+PHj437szDPPZNeuXUkPKtVSfQ+sJ+edMZzF66sI6WbM5eTzzhge8/n9KfTIlns16ZTUWPJPwfa1uQS3rUGPKsTw5p+C98h7dNUuyirfk97EEW+rcH/TIdy2HEIhvf15qqmxv+lQr7++bPmeDITktmPHDnbv3j0ghhH3mMB8Ph8tLS3k5nZetbS2tiatG31ZWRkHDhxA13U0TUPXdQ4ePEhZWVmn5+3du5exY8MH4B1XZNkg0SpFKfSwtu4KMbqrUqR0UnoD7ad4/RVfrVqOU3USMIIyhiVBH+w4wLJ3dnGg3suwIjezv3ky40cPS3scO3bs4J133hkcCewrX/kKb775ZtxpnevXr2f06NFJCaS4uJjRo0ezcuVKZsyYwcqVKxk9enTM9iHA9OnTWbp0Keeffz4NDQ1s2LCBxYsXJyWGdOrpcrIUemS37qoUOTO7Eli8/op+AiiKgq7rMoYlAR/sOMDTy7Zhsynk5dg43NTG08u2weyx/U5ibW1t3HHHHezatQubzcaJJ57IokWLeO2111iyZAm6rpObm8t9993HkCFDeOKJJ2hpaWHGjBmcddZZ3H333WzatInHHnsMXdcpKipi/vz5nHDCCXz22WfceeedtLW1YRgGs2bN4rrrruP999/n5z//OX6/v31ky8UXX5yk71biekxg3//+9/nxj39MU1MT559/PqWlpRw6dIh169bx1FNP8fjjjyctmPvuu4958+bx1FNPkZ+fz4IFCwC4/vrr+dGPfsTpp5/OjBkz+Mc//sH5558PwM0338zxxx+ftBisIhlDNEXfJKNTfDZVKfaky/6KoTb+36hZUoWYgGXv7MJmU3A5wv9Nuxw2fIRY9s6ufiewP//5zzQ1NbF69WoAGhsb+eCDD1izZg2LFy/G4XCwceNG7rrrLn7/+9/zox/9iHfeeYcnnngCCBfQ3X777bz00kucfPLJLF26lNtuu42lS5eyZMkSJk+ezM0339z+2hBe2CxZsgRN06itrWX27Nmcc845FBQU9Otr6a0eE9g3vvEN/uu//ouHH36YhQsXtj9eVlbGgw8+yDnnnJO0YEaOHMnSpUs7PR6ZIArh4o77778/ae9pVcluRyUS058LytHUvFKM1oaUtItKt+4mNssYlsQcqPeSlxP7z63TrnGw3tvv166oqOCzzz7j/vvv5+yzz+ab3/wmb731Fjt37my/I2uaZty5jgD/+Mc/qKio4OSTTwbg8ssv5/7776elpYWzzjqLBQsWEAwGmTBhAl/72tcAqK+v56677uLzzz9H0zQaGxvZvXt32juD9JjA2tra2L59O6NGjeJf/uVf+M53vkNhYSEnnXRSOuLLav0pwpAu9JnR3wvKEfaxF+Lf/CJmELA5wiX3HdpFpWomWLLJxOb+G1bkDlceO47+k+sP6gwtcvf7tY8//nhWr17NX/7yFzZt2sTjjz/Oeeedx+WXX95lBXk00zS77HF7wQUX8NWvfpXNmzfz7LPP8sc//pFHH32U++67j6lTp/KLX/wCRVG44IIL8Pv9/f5aeqvHe2Dz58/n7bffZuTIkezatYtVq1ZJ8kpApAijoTUQU4Sxrbo2oc8fO7KEudNGUehx4PWFKPQ4mDttlJx/pZjRfCiccKL1YevPXj4W56RrUD2FEGhF9RTinHR0FRdZ6RmtDTErvUT7KaZTvP6K/bk/NhjN/ubJhEImvkAI0wz/GgqZzP7myf1+7f3796NpGpWVldx5553U19czdepUVqxY0X7FSNf19h62ubm5NDcfrbQ844wz2LFjB9XV1QC89tprfOUrXyE3N5fPP/+c0tJSZs+ezc0338yHH34IQHNzM8ceeyyKorB582Y+//zzfn8dfdHjCuzdd99l2bJlDB06lGuuuYa5c+fy05/+NB2xZbVkFGFIF/r062rrT7Hn4F25oFerpW6rFJO00ksX2Srsn/Gjh8HssSx7ZxcH670MTWIV4ieffMLPfvYzAAzD4IYbbuCss87ixz/+MTfddBO6rhMMBpk+fTqnnXYaEydO5Pnnn+eyyy7j7LPP5u6772bhwoXcdttthEIhioqKeOSRRwBYs2YNb7zxBna7HUVRuOuuuwD4yU9+wv3338+zzz7Lqaeeyqmnntrvr6MvepwHNm7cOLZu3dr++7PPPpu//vWvKQ8sldJxD6yrmWCHm/0cW+Lhy9pWdN3EpikML/EkvZlvttyrSadEYok5Azuy9WcGvGCaKE5PzHZg9Iqqt3HsfuL74PDEnQn2+bR/S0thRLb92VghjoFwD2wg6XEFpus6f/nLX4jkuVAoFPN7gIkTJ6YuwiwVrwijqTWAz6+zv96L1x8CE/xBkwOH2+SOl0WEE1Jsp3hD00DX+7Vaij7vChYdg2LPwQwFOq30PinI5/U4d65kZpcQnfWYwIqLi9uXjQCFhYUxv1cUhT/96U+piS6LxSvCaPWFyHXb8fpDKICqKhimQps/RGGeU+54WUTHrb+Wl2/rV0l8x8rGUMthDG8zRFZfUau6TUM8aIrR6c7VhpqNksCE6KDHBPbWW2+lI44BJVJ96Avo6HqofZvQ2xYkz22nqTWAeuTfLlWBkB6uMKz+spHbf/WezAezmP6WxHc871JtLhSnAZqG6sqLOVc7/MUq3FrnO1d7W/ezaOvTct9KiCg9ViGK3omuPhyS5yQ/14HToTF9QjnDSzwEQgY2m0pkA9Yww6vY+iYfiqL0qWJRpJZ97IX9mqDcVWUjQR/uS+4g98pHcV9yB/bysRS7iggYwZinNgVa8Ol+6fouRAd9mgcmutZd9WFkWzHHodHcZqAbJuHZuAqgUJDrQFEUaRtlMfHOxbqrQux4vwu7K7xF2MUKLrpRrktz4Q22AbTfufKGvOTaPbKtKEQHksCSrKcWUE6bSkNzANMwsdtUnHYbbX6dgjw7bpc97ueIzEt0gnK8Th6RKkYgfHYW9LWv4Do2ym0OtuINtdEa9KIqKsPcpbg0F3n22DM4h2qnzlcfJ4KeRRLm4WADQ+yFsh0pspZsISZZSYGLQMiIeSwQMnA5tPAIFRPKStwMLXKT73Fw7cWjGXlsPjab1ulzpG1U9ok+71IUBUwDAm0QaMVsa8RsrceWO6S9BD+6Ua5P99MSaAHArtkozhmCX/eT78jrtK3Y167v0UMoc+1u2Y4U3frwww/5yU9+0ufP37JlC7Nnz05iRLEkgSXZ9Anl6LqBP6hjmib+oI6uG2Ca7VuL4S7eBo0tAX657ENa2oJ424KdPkfaRmWf6PMuM9CG2VIXTmKAklOAYndRMHFG+2quzlePQw2vvJsCLaAoqCiEDB2n5kBTNUzTRDd0/Hog/PdDD/S5lVN0wlQUpf09NtRsTNJ3QGSbUCjU5cdOP/309kvS6aTres9PQrYQk66rWV8vratq31r0+oLUN/sxDRPDhL11XhTArimEQqpUIWax6IpFs63xSKm8Caod5cj9scb3V2C/4DYgtlFuyAihKiomBjY1vCLXDZ06/2FcNid6SMem2CjzDOvztl9XneWlyjH1Wqu30vj+CkINB7EVDqVg4gw8I8f1+3V/+ctf0tjY2H696fDhw0yfPp0NGzbw1FNP8be//Y1gMMioUaO477778Hg8zJs3D4/Hw549ezh8+DCLFy+OO5Jly5YtLFiwgGXLlgHw9ttv8+STTxIKhVBVlYcffpiKiooux7F0tHz5cp577jkAysvLmT9/PsXFxSxbtoxVq1ZRVFREdXU1Dz30UEKjuiSBpUC8FlAlBTXtF5ubvMH25AWgKeFqxCZviJtnnyaJK4P622A3polvKHj0rpcrP/yrzUGo4SCR087oRrmaoqGb4Z888+y5tIV8HPY1oKoaQ5wFMU10E0ku8aYox+ssH6lyPOA9hE/3cdjfyO6PPuf88qlcdFJlwl+76Fpr9Vbq1v4PaDYUVy6hlsPh30//t34nsVmzZjFnzhxuv/12bDYbK1euZOrUqbz44ovk5eXxhz/8AYBHHnmEZ555hltvvRWAv//977z00ku43W7Wr1/faSRLR7t37+buu+9m8eLFjBgxgkAgQCAQ6HYcS7SqqioeffTR9taEP//5z3nggQf4+c9/DsDWrVtZsWIF5eWJ7zzJFmIKbauuZeGSrdz+q/ditglDIaM9edk0BUVR0FQFwzRYu6Ums0EPYslosBvTxFdVQVHBU4TqPNJ1PBTAVji0/fnRjXKdmgNVUci1e8ixuWjwN4ECBY68Xm/3RZ91RZfen1I4stN2pDfkxaE6aAm2ohs6GiqGafJmzVtyNpYkje+vAM2GanehKAqq3QWaLfx4Pw0fPpyRI0eycWP478Vrr73G5ZdfzltvvcXrr7/OjBkzmDFjBm+99RY1NUf/fZk+fTpud/jvZfRIljVr1uBwODq9z3vvvcfkyZMZMWIEAA6Hg9zc3LjjWHbs2EFLS0vM52/ZsoUpU6YwdGj47/+3v/1t3n///faPjxs3rlfJC2QFljKR+2Ah3cTrC3K42QAU7JqCqijomGgqqEd+QjdMsGlSeZhJSRulcqRi8WhFYvgcK9Jto2DiDKKnQEU3yo1eNZmYDHEU4rYf3fJLtPqwqynKnzZUM2fUzKNViI5CvME2AkYg3B1GCf9Mq6KgG7qU6idJqOEgiis35jHF5iTUcDAprz9r1iyWL1/O8ccfT3NzM+PHj8c0Te69994uW/1FkhfEH8nyxhtvxDy/q7a53Y1j6c3zPB5Plx/riqzAUmTtlhpCukmzN4BugKaqmKZJfUsAu01BUcA4UqwYXo2ZuF12qTzMoGSNUonoaqRKd1tGY0oquGXc95n/9Ts5Kf8ENK1DdWqC1YfRxSERkeQXeY9fXvIgt4z7PmWeYQSNEApRjYUxsak26nz1bK/dyaKtT3PPe//Noq1Py6qsD2yFQzFDsfOyzJA/ZjXeHxdccAF/+9vfeP7555k1axYAU6dO5YUXXsDnC/9Q3NLS0j4ypaN4I1kaGhpinnPOOeewadMm9uzZA0AgEKClpaXbcSzRJk6cyMaNGzl0KPzf06uvvsrXv/71fn3dsgJLkdpGH15fEFBQFTDMo2deumHicdloaQsRDBk47CpulwObpkjlYT/09/wqFVOUE70/Fk9/Bkl2N0U53vvs/uhzDNNERcHEBNPE7cjBpbli7qlJc+G+KZg4g7q1/4OBD8XmDCczPbwaT4acnBzOO+88li1b1t6b9oYbbuAXv/gF3/rWt1CU8FHFv//7vzNy5MhOnx9vJMuwYcPakxXAiBEjeOCBB7j11lvRdR1N03j44Yc59dRTuxzHEu2UU07hJz/5Cddeey0QXvXNnz+/X193j+NUBqJ0jFNZuGQrn37RgKaGF7lB3cA0w2f6mqpwbGkuTa0B/AEdt8uW9MrDbBlPkSzxxqB0HHnSVSyRxKfXfwFBH7jyUFx5/R6b0pXefE/iFWIkWsARSTzRyS96EGV0HKs/28CbNW9hGAY21YbbnoNN0XCoDnT0mETo1wMUOPK5Zdz3+/DVx5ctf1/7M04lVVWIg5mswFJk+oRyqr9sQjdMVOVoIwaF8FkXQJ7bjqYqLLypf8to0ffzq+jEp3iKMNuawNeMaYTQhhzb61VcsvV1kOSYkgrmMDPh5HfRSZWckH9cp+e/UvVa3LL7vnYBGcw8I8dJwkoySWApMnZkCRdPLGfVX2rQDfPodSBVId8T/mlWum0kj9F8qE8jTzomPsVdgGl3oXoKcV9yR6rCTVhfV2DQ++QX7/nFNYlvRQqRblLEkUKXnXMSN886jVHHFZCbY8emqeS5HbgcmnTbSDI1rzS85RctgfOreIUbph5CP7CLlpdvw7tyQa/K6JOpq1L4dBZRVJZPSVoXECGSTVZgKRZ9qTkyJyy6Q4dcWk6OmAvEUWdgPY086Vi4Yfi90FoPqhZzFwySew6WiK5K4dNZ2t7brUgh0kkSWJpI8kqt3ow8ia5WxO4Kd4uHcOLzhkuHFc+Q8J2VPt4FS4au2j6l+/ypr+dwQqSaJLA0iFxq1jQ1ZmAlIEksiRIpWe847oRQAEwTRbNhBloBE3KLURxRiaMfd8H6IpJgC7V6mm0aTndhezxy/iTEUXIGlgbRQy4jAys1TZW2URnQcdyJYneCZsNoqQs/QbOD0aE7dz/vgvUqvqh2VpMDDkKmga+1DsPvlfMnITqQFVgSRLYH61sCFOU6Om0P9jTkUqRPx2pFM9AG3nDjUjOyIgt4MdqawD0ERbMldJaWLNEJdrQOik9hoyNAva+R0qIT5fxJJOzll1/G7/fz3e9+t9ef+5//+Z/MmjWL8ePHd/u8RYsWccopp3DRRRf1Mcr+kQTWT9Hbg3k58bcHSwpc7Z3oI6SEPjM6Fm2ER54AqOA9HP7/igqGDq11MGQ4zolX9niW1pfOH/F0TLAVIRunBjUItJJbmbyLwyL9/r7vI17fuZ6DrXUM9RRzWcU0zig7LWXvd+WVV3b5sUgnja489NBDCb3HLbfc0uu4kkkSWD/F2x70H3k8ksCmTyhn8foq/IRXXoGQISX0/dTX5NG5WjF4JGkdeYKihrvImwZKXimqK6/L5BV9lpasasVUtLMSmff3fR/x3P++gl3VyLW7aWhr5Ln/fYXrzqTfSayreWCRSch33HFH3HlbdrudO++8k7a2NioqKqipqeGmm27i3HPP5ZprruHaa6/l3HPPZd68eTgcDvbs2cP+/fv56le/yoIFC1AUhXnz5nHaaadx9dVXEwgEePzxx3n33XdRVZXjjz+eX/7yl3zyySfcf//9tLW14ff7mTNnTp9WhfFIAuunRLYHuxpyKQUcfdOf5NGxWhG7E2xO8DWFkxcAJmi2bos3uuv8Efl4X1ZmiVwH6M/lZpEZr+9cj13VcNrCf1+cNieE/Ly+c32/E1hX88Dcbjde79G5Bx3nbc2ePZt//dd/ZcaMGXz44YfMmTOny/f49NNPeeGFF1AUhVmzZvHee+8xadKkmOc888wz/POf/2TZsmU4HA7q68PVssceeywvvPACDoeD1tZWrrjiCr7xjW/E7cnYW5LA+inR7cF4Qy5F3/R37El0tWIkGZqKBuaR8QCmiZJT0O3Kp6vOH3r9Fxj9WJn1dB0gusehNNfNHgdb68i1u2Mec2gODrbW9fu1o+eBnXfeebz22mvcddddMbO2IHbeVktLC1VVVVx66aUAnH766Zx66qldvkdlZSVOZ/i/t6985SvU1NR0SmBvv/12+2oNoKgoXC3r8/m47777+OSTT1AUhYMHD7Jz586kJDCpQuyn6RPK0XUDf1APdyqQDhspl8yxJ5GRJ0rB0CMJTAFP0ZFzsK6LN7rq/IGhd65yVG3tK7NEY3Jfcge5Vz6K+5I7YhJf9OXm3g65FJkz1FNMQI/9+xLQAwz1FCfl9SPzwKqqqtrngXUUPW8rMpsrkTleQHvyAtA0DV3XOz2nq77wjz32GKWlpbz22mu8/vrrjB07Fr/fH/e5vSUJrJ/Gjixh7rRRFHoctLSFKPQ4mDttlKy2UqivbaPiiZylEfShDBmOWjAMBaN9dldXqyb72AvBCGEG/ZimiRn0h8vvj2w9xkjiPbLu5nwJ67qsYhpBQ8cfCv998Yf8BA2dyyqmJeX1480D605eXh4nn3wyK1euBGD79u1UVVX1K4apU6fy29/+lkAg/N9mZAuxubmZY445BpvNRlVVFR988EG/3ieabCEmQWR70CojIQa6vraN6ijepWbTCOGc9J2EztL0Q+cQ/PDN8AgWuwv76Rdg7NuZ1CKMjsUqQ4odNBtBaa6bZc4oO43rziRlVYjx5oH1ZMGCBdx111385je/YcyYMVRUVJCX1/dxMTfccAM/+9nPmDlzJna7nRNOOIEnnniCm266idtvv53XX3+d8vJyzjrrrD6/R0cyDyyJrJTArBJLquLoSxVix1i8KxdgtDYcPUsDzKA/oU70Xc0fs406h1DVn/s0lyyR99hpC/F6aR42R06Xc74SZZW/I2CdWFI5D8xqvF4vOTk5KIrCrl27uOaaa1i7di0FBQWZDi1hsgITWak/k44j+jqCBbouJDH27cQ5KbGejH15j4ogKC3w52PypQpR9MvWrVtZuHBh+9nVAw88kFXJCySBiUGsP3euukt+yUiu3b3HqY1NnHnRPf1+fTG4nXPOOZxzzjmZDqNfpIhDDFpdFWIkcpaWzEKSTL6HENlMEpgYtCIl9KqnEAKtPVYexnxuP5JfwvGl4T2EyGayhSgGtb5u9/Vm/lh/Ykv1ewiRzSSBJcm26lr+9Idt7DvUIq2iBriOFZCJlN33VbLO04QYiCyRwNra2rjzzjvZvn07mqZxxx13cO6553Z63pYtW7jhhhsYMWIEAA6Hg6VLl6Y52s4iHemdDk0GVg5wqWrim2zSL1EMBpZIYM899xwej4f169ezZ88e5s6dy7p162Jan0SMHDmSZcuWZSDKrkU60rscNoIhI25HepE9urtj1t8+jOkg/RLFYGGJIo41a9bw7W9/G4ARI0Zw2mmnsWnTpgxHddS26loWLtnK7b96j4VLtrKtujbm47WNPhy22G+lDKzMTtETkaNXWMGabUBy+zCmivRLFIOFJVZge/fu5dhjj23/fVlZGfv374/73D179jBr1ixsNhtXXXVVQn2/Oiouzk34uR/sOMDv/7QLm02hINdBiy/I7/+0i4ICN+NHDwvHW5rL4aY2AOxHEpkvEKKsNDejN/et0jXAKnFAz7HsfXMdmsOBaj8yTcBuwwj6YMc6Ss+cRLDoGEIth1FtR6cNGEEftqJjevV1pvJ7cjjYQK7DHdOoVdNcHA42dHrfbPqzSRerxCF6lpYENmvWLPbu3Rv3Y++9917CrzNmzBg2btxIXl4e//znP/ne977HsGHD+PrXv96reHrTSuqVdTtBAU1VCelm+FdF55V1OzmhJDwe4bwzhrefeamK0j6w8rwzhmesPU62tOZJp0Ri8dfvD6+8Qke7bZvY0Ov3hz939Pnom19EDxmxLaRGn5/w15ms70lX51xD7IU0Bppi+iX69QBDHIUx75ttfzZWiEOSm7WkJYG99tpr3X58+PDhfPnll+3zY/bt28eECRM6PS839+jK6fjjj6eyspKtW7f2OoH1Rm8GVv7p73ulCjFLRc69TG8jeJvAMwTFkRP+YNTl4b6Utnc8U2udPBvyT+lXvN2dc1WWT+HVquX4CcT0S6wsn9Kv9xTCaiyxhTh9+nReeeUVTj/9dPbs2cOHH37Iz372s07PO3jwIKWlpSiKQkNDA5s3b+aWW25JaWy9GVh53tdOtMRPkaJ3YioLcwqhtR6zuRYztxhF1TpdHu5NaXu8qsW6tf+D7Wtz+1X0EX3OBeDUHPgJsKFmI7eM+z5zmClViGLAs0QCu+6665g3bx7Tpk1DVVXmz5/fvtpatGgRQ4cO5corr2TdunW8/PLL2Gw2dF1nxowZVFZWpjS26RPKWby+Cj/hlVdke1AGVg4c0ZWFit2JoSjgbQDvYdRhJ/fr8nC8qkXMYL+rFut89bhtOTGPRc8FG1NSIQlLDHiWSGBut5snnngi7seiV1hXX301V199dbrCAo5uD67dUkNtoy+h7cFt1bW9er7IrI5Nc1WnG9ORA4HWHseq9Pa1ARSbE70XVYurP9vAW19swh8K4LQ5mHrcZIpdRZ3OuWQumBhsLJHArC4ysDIRkUvNmqbKpWYLCdZsY++b6/DX7+90btWfrvQ9iffaZsif8Guv/mwDa/ZsQFEUVEUloAdZs2cDZw79F+p9h+WcSwxqlrgHNpBELjU77Vr4Do5dQ9NU1m6pyXRog1bkHCrUcjju3a5UNs2N+9p64q/91hebUBQFTVFRj/yqKAof1e9gUo7cDQAAEJJJREFUzqiZFDjy8YbaKHDk92mopRDZTFZgSZZI1aJIr8g5lGp3hcvjO3TPSGXT3HivXTx5Nt4OVYhdlcT7QwFUJfbnTAUFfygg51xi0JMElmSJVi2K9Elk8nIqm+Z2fG1PaR7eqGrV7krinTYHAT0IHL2UbGLi7NgNRIhBSLYQk2z6hHJ03cAf1DFNE39Ql6rFDLP6YMjuWj9NPW4ypmmimwbGkV9N02TqcZMzHbYQGScJLMnGjixh7rRRFHoceH0hCj0O5k4bJQUcGRQ5hzKCPksOhqzz1eNQ7TGPRUriLzqpkgtHVOLQ7BimgUOzc+GISi46KbXXR4TIBrKFmAK9qVoUqRc5h2LHOvQ4VYiZ1lNJ/EUnScISIh5JYGJQsJePpfTMSZbslCKtn4ToG9lCFCLDxpRUSEm8EH0gKzAhetDdgMtkkZJ4IXpPEpgYEHqbZBJ9frxmvP7NLwLXWOYMTYjBSrYQRdbraYpyf54f0+hXUcJNeVVb+HEhREbJCkxkvXgd3402P763fo3f6W5fYVE6qcvnR3fmiJbIJWghRGbICkxkPaP5UHg68hFmoA28jRD0x6ywWqu3xn0+0GVSsvolaCEGM0lgIut1TDJmW2O485LNDkEfpvcwZks9h177OcGabb1KSqls9CuE6B9JYCLrdUwyhILhD9icmC11YOigqBhBH/7NL6KWVSSclOzlY3FOugbVUwiBVlRPIc5JUsAhhBXIGZjIeh07vmN3gs0JwTZQlPD/MFA0O6g2jH07cU5KvPt8Khv9CiH6ThKYSLl03KOKTjKRKkOzLQiKChhgguouxNDCZ12SlITIfpLAREq1Vm9N+z2qyIrM99avw4Ucmh1c+WguD0ab1/IFGF3NBhNCxJIzMJFSje+vyMg9Knv5WFxTb0TJLUJxD0Fx5GAEfZYvwIjMBmsMNKGgsLuphl9ve4GH/vIY22t3Zjo8ISxFEphIqVDDwYRL1pOtYwGGLXeI5QswIrPBDNOgwd+IiYmiKBzy1fFq1XJJYkJEkS1EkVK2wqHoDXXhwoqINN6jij7rKi3Ns2Q3+mh1vnrcthwOttWBoqCiYAKGabQPuZTtRCHCZAUmUqpg4gy5R9ULxa4iAkaQkBFCQQHAxMSmau1DLoUQYZLAREp5Ro6Te1S9UFk+Bd3QURUVwzQwTAMTyLPnxgy5FELIFqJIA6uWrKejvL+3xpRUMIeZrKhew77WA6iqRqE9D03VZMilEB1IAhODkpXHpERmg0WX0xc48qWcXogOJIGJQak3HekzRYZcCtE9OQMTg1JvOtILIaxJEpgYlGRMihDZTxKYGJRkTIoQ2U8SmBiUZEyKENlPijjEoGXV8n4hRGJkBSaEECIrSQITQgiRlSSBCSGEyEqSwIQQQmQlKeJIkW3VtazdUkNto4+SAhfTJ5QzdmRJpsMSQogBQxJYCmyrrmXx+io0TcXtstHQGmDx+ioASWJCCJEksoWYAmu31KBpKk67hqIoOO0amqaydktNpkMTQogBQxJYCtQ2+nDYYr+1DptKbaMvQxEJIcTAIwksBUoKXARCRsxjgZBBSYErQxEJIcTAY4kEtmLFCi699FK+8pWv8NJLL3X73FdffZVp06ZRWVnJ/PnzMQyj2+dnwvQJ5ei6gT+oY5om/qCOrhtMn1Ce6dCEEGLAsEQRx+jRo3n88cd55plnun3eP//5T37xi1+wfPlyCgsLuf7663n99deZOXNmmiJNTKRQQ6oQu2fFichCiOxhiQQ2atQoAFS1+wXhm2++SWVlJUVFRQBcccUVLFu2zHIJDMJJTBJW1yITkU09BAEvesth9AO70L96Ca4zZ2Q6PCFEFrDEFmKi9u3bx/Dhw9t/P3z4cPbt25fBiERfBbetCScvXzOYOqgqmAbB/1tJsGZbpsMTQmSBtKzAZs2axd69e+N+7L333kPTtHSE0a64ODdlr11ampey1+4tq8QSL46a1jqUYBumoqAoR36OUhRMQ4cd6yg9c1LaYskEiaMzq8RilThEz9KSwF577bWkvE5ZWVlMIty7dy9lZWW9fp26uhYMw0xKTNFKS/M4dKg56a/bF1aJpas4TE8xZlMdqComR/4sTBNUG/76/SmJ3erfk8EaB1gnlp7ikORmLVm1hXjBBRewYcMG6uvrMQyDpUuXcuGFMkE3G9nHXti+bYhJOHmZJjg9qHmlmQ5PCJEFLJHAVq5cyeTJk1m7di2LFi1i8uTJ7Nq1C4BFixbx8ssvA3D88cfzgx/8gDlz5nD++edz3HHHcdlll2UydNFH9vKx2L96CSgqGHr415x8FFULJzchhOiBYppm8vfSLE62EK0TRzpL6bPlezLY4gDrxCJbiNnFEmX0YvCyl4+Vu19CiD6xxBaiEEII0VuSwIQQQmQlSWBCCCGykiQwIYQQWUkSmBBCiKwkCUwIIURWkgQmhBAiK0kCE0IIkZUkgQkhhMhKksCEEEJkJWklJUQCttfuZEPNRup89RS7iqgsn8KYkopMhyXEoCYrMCF6sL12J69WLacx0ITblkNjoIlXq5azvXZnpkMTYlCTBCZEDzbUbERTNZyaA0VRcGoONFVjQ83GTIcmxKAmCUyIHtT56nGo9pjHHKqdOl99hiISQoAkMCF6VOwqImAEYx4LGEGKXUUZikgIAZLAhOhRZfkUdEPHrwcwTRO/HkA3dCrLp2Q6NCEGNUlgQvRgTEkFc0bNpOD/t3e3IU3tDxzAv9ucmlHO6mqCXSLMp0gUSq0Mcgaz2my96Po4emEFWeQbQ0V6tszeSJAolGFP9K42ml1TmmglqVHgQlGzR2jLaxneTJy6838R/5H/6l79Yzs7+v28Og87O9/Dpt+dw+F3vBfiy/gI/L0X4o8wPe9CJBIZb6MnjzX2pgNjHX/C+fdfkC/4DcroLaI9vXnVkggWFpGH4RkYeaSxNx0YfXgVzuFPgPd8OIc/YfThVYy96RA7GhF5CBYYeaSxjj8BuRdkSh/IZDLIlD6A3OvrciIisMDIQzn//gvw8p680Mv763IiIrDAyEPJF/wGjDsmLxx3fF1ORAQWGHkoZfQWwDkOYWwUgiBAGBsFnONflxMRgQVGHkr5ezR8Nhggn68CHMOQz1fBZ4NBtLsQicjz8DZ68ljK36NZWET0UzwDIyIiSWKBERGRJLHAiIhIklhgREQkSSwwIiKSJBYYERFJEguMiIgkiQVGRESSxAIjIiJJYoEREZEkzcmhpORymSTfe7o8JYun5AA8JwtzfM9TsnhKDvp3MkEQBLFDEBERTRcvIRIRkSSxwIiISJJYYEREJEksMCIikiQWGBERSRILjIiIJIkFRkREksQCIyIiSWKBERGRJLHAZlhlZSV0Oh30ej22b9+OO3fuiJbl+PHjSElJQWpqKtLT02G1WkXJYTKZoNPpEBUVhWvXrrl9/y9fvkRaWho0Gg3S0tLw6tUrt2cAgLKyMqjVaoSHh6Onp0eUDAAwODiIPXv2QKPRQKfT4cCBA/j48aMoWXJzc5Gamgq9Xo/MzEx0dXWJkuNb58+fF/0zoikSaEYNDQ25pu12uxAbGyt8+vRJlCwWi0VwOByu6eTkZFFydHd3C729vcKhQ4eEq1evun3/BoNBMBqNgiAIgtFoFAwGg9szCIIgtLe3C+/evROSkpKE7u5uUTIIgiAMDg4Kjx49cs2fOXNGKCoqEiXLt38vDQ0Ngl6vFyXHfz179kzIyckRNm3aJOpnRFPDM7AZtmDBAtf0ly9fIJPJ4HQ6RcmSlJQEpVIJAIiJiYHdbhclS1hYGEJDQyGXu//r9uHDB3R2dkKr1QIAtFotOjs7RTnjWLNmDYKDg92+3/+lUqkQHx/vmo+JicG7d+9EyfLt38vnz58hk4k3kK7D4cCJEydw9OhRUXPQ1M3J0eh/tRs3buDy5cuw2+04ffo0AgICxI6E69evY9OmTaKUiJhsNhuCgoKgUCgAAAqFAoGBgbDZbFi0aJHI6cTndDpx48YNqNVq0TIUFxfj4cOHEAQBFy9eFC3HuXPnkJqaimXLlomWgaaHBTZNO3bs+Omv1ZaWFigUCmRkZCAjIwPd3d3Iz8/HunXrfkmJTSULANTW1uL27du4fv36jGeYTg7yPCdPnoSfnx+ys7NFy3Dq1CkAgNFoxNmzZ3HhwgW3Z3j69CmsVivy8/Pdvm/6/7HApunWrVtTfm14eDgCAwPR1tYGjUYjSpaGhgaUl5ejpqYGS5YsmfEMU80hluDgYLx//x4TExNQKBSYmJhAf3+/R1zKE1tZWRlev36Nqqoqjzgz1+v1OHLkCAYHB91+1aK9vR0vXrxAcnIyAMButyMnJwelpaVITEx0axaaOvG/tbNMX1+fa/rt27fo6upCaGioKFkaGxtRWlqK6upqhISEiJJBbIsXL0ZkZCTMZjMAwGw2IzIycs5fPiwvL8ezZ89QUVEBb29vUTIMDw/DZrO55i0WC/z9/aFSqdyeZe/evXjw4AEsFgssFguWLl2K6upqlpeH4wMtZ1heXh6eP38OLy8vKBQK7N69G1u3bhUlS0JCApRK5aR/1jU1NW7/dWs2m3H27FkMDQ1BqVRi3rx5uHTpktuKva+vD4WFhRgaGsLChQtRVlaGFStWuGXf3yopKUF9fT0GBgYQEBAAlUqF2tpat+fo7e2FVqvF8uXL4evrCwAICQlBRUWFW3MMDAwgNzcXIyMjkMvl8Pf3R0FBAVatWuXWHD+iVqtRVVWFsLAwsaPQP2CBERGRJPESIhERSRILjIiIJIkFRkREksQCIyIiSWKBERGRJLHAiIhIklhgNCup1WpER0cjNjYW69evR1FREYaHhwEA9+/fR1ZWFmJjY5GQkIDs7Gzcu3dv0vatra0IDw//4bBGhw8fhkajQUREBG7evOmW4yGi77HAaNaqqqrC06dPcevWLVitVlRWVqKurg55eXnQ6/Vobm5GS0sLDh48iMbGxknbGo1GqFQqGI3G7943IiICx44dQ1RUlLsOhYh+gGMh0qwXFBSEjRs3oqenB2azGbm5udi5c6drfVxcHOLi4lzzIyMjqKurQ0lJCQoKCmC1WrF69WrX+qysLACAj4+P+w6CiL7DMzCa9Ww2G5qbm+Hr6wubzfavAyvfvXsX8+fPR0pKChITE2EymdyUlIimgwVGs9b+/fuxZs0aZGZmYu3atdi1axcAIDAw8B+3MxqN2LJlCxQKBbRaLcxmM8bGxtwRmYimgQVGs1ZFRQUeP36MxsZGHDt2zDXKeX9//0+3sdlsaG1thU6nAwAkJydjdHQUTU1NbslMRFPHAqM5Y8WKFQgODkZ9ff1PX2MymeB0OrFv3z5s2LABmzdvhsPh+OHNHEQkLt7EQXOGTCZDYWEhiouLoVKpoNFo4OfnhydPnsBkMuHkyZMwGo04cOAA0tPTXdt1dHQgLy/P9aBFh8MBQRAgCALGx8cxOjoKpVLpEQ+FJJpL+DgVmpXUajVKSkqwfv3679Y1NzejqqoKXV1d8PHxwcqVK5GTkwOVSgWDwYCmpqbvHni5bds2ZGRkIDs7GwaDAW1tbZPWX7lyBfHx8b/0mIhoMhYYERFJEq95EBGRJLHAiIhIklhgREQkSSwwIiKSJBYYERFJEguMiIgkiQVGRESSxAIjIiJJYoEREZEk/QfHmccbOLu72QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 453.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['PCA1'] = X_2D[:, 0]\n",
    "iris['PCA2'] = X_2D[:, 1]\n",
    "sns.lmplot(\"PCA1\", \"PCA2\", hue='species', data=iris, fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n",
    "This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before.\n",
    "\n",
    "我们发现在二维数据表示中，花的种类也是很容易分开的，即使在PCA算法对于种类标签根本没有了解。这也体现了这个数据集可以相对直接的进行分类，就像前面看到的那样。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning: Iris clustering\n",
    "\n",
    "### 无监督学习：鸢尾花数据集聚类\n",
    "\n",
    "> Let's next look at applying clustering to the Iris data.\n",
    "A clustering algorithm attempts to find distinct groups of data without reference to any labels.\n",
    "Here we will use a powerful clustering method called a Gaussian mixture model (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\n",
    "A GMM attempts to model the data as a collection of Gaussian blobs.\n",
    "\n",
    "下面我们来看看将聚类算法应用在鸢尾花数据集上的情况。聚类算法试图在没有任何标签的数据集中找出不同的分组。下面我们会使用一个强大的聚类方法称为高斯混合模型（GMM），我们会在[深入：高斯混合模型](05.12-Gaussian-Mixtures.ipynb)中详细介绍它。GMM试图将数据看成是一组高斯斑点。\n",
    "\n",
    "> We can fit the Gaussian mixture model as follows:\n",
    "\n",
    "我们可以如下拟合高斯混合模型：\n",
    "\n",
    "译者注：GMM因为过时，下面代码已修改为GaussianMixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture      # 1. 选择模型类型\n",
    "model = GaussianMixture(n_components=3,\n",
    "            covariance_type='full')  # 2. 实例化模型，设置超参数\n",
    "model.fit(X_iris)                    # 3. 拟合数据，注意y没有设置\n",
    "y_gmm = model.predict(X_iris)        # 4. 预测值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results:\n",
    "\n",
    "想之前一样，我们会给鸢尾花`DataFrame`添加聚类列，然后使用Seaborn绘制结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1173.85x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['cluster'] = y_gmm\n",
    "sns.lmplot(\"PCA1\", \"PCA2\", data=iris, hue='species',\n",
    "           col='cluster', fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n",
    "This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\n",
    "This sort of algorithm might further give experts in the field clues as to the relationship between the samples they are observing.\n",
    "\n",
    "使用聚类编号将数据分开，我们可以清楚的看到GMM算法运行的多么良好：*setosa*种类被完美地分到了群组0，剩下的*versicolor*和*virginica*有一点混在一起，但是也比较准确。这意味着即使在没有专家告诉我们如何区分不同种类的花的情况下，我们也可以使用计算机*自动*根据聚类算法将它们区分出来。这种算法还可以为专家提供他们观测的样本之间联系的线索。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application: Exploring Hand-written Digits\n",
    "\n",
    "## 应用：分析手写数字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits.\n",
    "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library.\n",
    "\n",
    "要在一个更加有趣的问题中展示这些方法，让我们考虑一个光学字母识别的问题：手写数字的自动识别。正常情况下，这个问题包括了定位和识别图像中的字母。这里我们炒了一个捷径，使用Scikit-Learn自带的预处理过的图像。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and visualizing the digits data\n",
    "\n",
    "### 载入和展示数字图像\n",
    "\n",
    "> We'll use Scikit-Learn's data access interface and take a look at this data:\n",
    "\n",
    "我们使用Scikit-Learn的数据访问接口来载入这些图像并且查看一下数据内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\n",
    "Let's visualize the first hundred of these:\n",
    "\n",
    "图像数据是三维数组：1797个样本每个包括8 × 8像素的图。我们可以展示头100张："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(digits.target[i]),\n",
    "            transform=ax.transAxes, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation.\n",
    "We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\n",
    "Additionally, we need the target array, which gives the previously determined label for each digit.\n",
    "These two quantities are built into the digits dataset under the ``data`` and ``target`` attributes, respectively:\n",
    "\n",
    "为了要在Scikit-Learn中使用这个数据集，我们需要一个二维的`[n_samples, n_features]`数据表示。在本例中我们可以将图像中的每个像素点当成一个特征：也就是说，通过将每个图像的像素数组平铺展开成一个长度为64的一维数组。除此之外，我们还需要目标数组，如上图一样是每张图标记的数字组成的数组。这两个量已经在数据集中內建好了，分别叫做`data`和`target`属性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = digits.data\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = digits.target\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We see here that there are 1,797 samples and 64 features.\n",
    "\n",
    "我们看到一共有1797个样本和64个特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning: Dimensionality reduction\n",
    "\n",
    "### 无监督学习：降维\n",
    "\n",
    "> We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space.\n",
    "Instead we'll reduce the dimensions to 2, using an unsupervised method.\n",
    "Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions:\n",
    "\n",
    "我们希望能够将我们的点在一个64维的参数空间中可视化出来，但是在这么高的维度上有效的可视化是非常困难的。所以我们转而使用无监督方法将维度减至二维。这里我们使用的是流形学习算法*Isomap*（参见[深入: 流形学习](05.10-Manifold-Learning.ipynb)），然后将数据转换成二维："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import Isomap # 选择模型类别\n",
    "iso = Isomap(n_components=2) # 实例化模型，设置超参数\n",
    "iso.fit(digits.data) # 拟合数据，这里也没有y参数\n",
    "data_projected = iso.transform(digits.data) # 转换数据到二维\n",
    "data_projected.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We see that the projected data is now two-dimensional.\n",
    "Let's plot this data to see if we can learn anything from its structure:\n",
    "\n",
    "我们看到映射后的数据现在是二维的了。下面我们把降维后的数据绘制出来看我们学习的成果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n",
    "            edgecolor='none', alpha=0.5,\n",
    "            cmap=plt.cm.get_cmap('Spectral', 10))\n",
    "plt.colorbar(label='digit label', ticks=range(10))\n",
    "plt.clim(-0.5, 9.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n",
    "Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n",
    "On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n",
    "\n",
    "上图给我们数据集在高纬度64维空间很直观的分布情况展示。例如数字0和1在特征矩阵空间很少重叠。这很容易理解：0在图像中间有个空白区域，而1中间没有空白区域。另一方面，数字1和4几乎有着很连续的图谱：当我们一直到一些人写数字1时会加上“帽子”时，这就容易理解了，这回造成两者看起来很相似。\n",
    "\n",
    "> Overall, however, the different groups appear to be fairly well separated in the parameter space: this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n",
    "Let's give it a try.\n",
    "\n",
    "大体来说，上图说明不同的数字在它们的特征矩阵空间中都能较好的区分开：这表示即使是一个很直接简单的有监督分类算法应该也能适合分类这个数据集。让我们试一试。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification on digits\n",
    "\n",
    "### 数字分类\n",
    "\n",
    "> Let's apply a classification algorithm to the digits.\n",
    "As with the Iris data previously, we will split the data into a training and testing set, and fit a Gaussian naive Bayes model:\n",
    "\n",
    "下面我们在手写数字上应用分类算法。就像前面鸢尾花数据那样，我们将数据集分为训练集和测试集，然后将这些训练数据拟合到高斯朴素贝叶斯模型中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(Xtrain, ytrain)\n",
    "y_model = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:\n",
    "\n",
    "我们已经预测了我们的模型，我们可以将得到的预测结果和测试集的目标向量进行比较得到模型的准确率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n",
    "However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn:\n",
    "\n",
    "使用这个非常简单的模型，我们得到了大约80%的数字分类的准确率。然而这个数字并不能告诉我们*哪里*出错了，输出*混淆矩阵*是一个好办法，可以使用Scikit-Learn计算和使用Seaborn绘制图表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "mat = confusion_matrix(ytest, y_model)\n",
    "\n",
    "sns.heatmap(mat, square=True, annot=True, cbar=False)\n",
    "plt.xlabel('predicted value')\n",
    "plt.ylabel('true value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights.\n",
    "Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\n",
    "We'll use green for correct labels, and red for incorrect labels:\n",
    "\n",
    "这给我们展示了哪些数字更容易被错误标记：例如比较多的数字2被错误分类到了数字1或数字8。另一种直观展示模型准确率的方法是绘制输入的数字图像，还有它们预测的标签。我们使用绿色展示预测正确的标签，红色展示错误的标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "test_images = Xtest.reshape(-1, 8, 8)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(y_model[i]),\n",
    "            transform=ax.transAxes,\n",
    "            color='green' if (ytest[i] == y_model[i]) else 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n",
    "To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach.\n",
    "\n",
    "通过检查这个数据子集，我们也能获得算法在什么情况下变现的不尽人意。要获得超越80%分类准确率，我们需要转向更复杂的算法例如支持向量机（参见[深入：支持向量机](05.07-Support-Vector-Machines.ipynb)）、随机森林（参见[深入：随机森林](05.08-Random-Forests.ipynb)）或其他分类方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "## 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API.\n",
    "Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n",
    "Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n",
    "\n",
    "在本节中我们介绍了Scikit-Learn数据表示方式和评估器API的基本概念和使用方法。无论使用哪种评估器，载入/实例化/拟合/预测这些步骤都是一样的。掌握了评估器API这些信息后，你可以自己阅读Scikit-Learn文档以及开始在数据上尝试使用不同的模型。\n",
    "\n",
    "> In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model.\n",
    "\n",
    "在下一节中，我们会讨论也许是机器学习中最重要的课题：如何选择和验证你的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
